Prioritization via an Algorithmic Model

Prioritization via an algorithmic model overcomes many of the deficiencies of prioritizing via unstructured debates. A simple Boolean-based (data with only two possible values) algorithmic model that is applied in a product features matrix will arguably yield a far better feature prioritization and faster. A Boolean-based model forces its users to make unequivocal decisions, whereas with a scale-based model the participants will very quickly gravitate to repeatedly selecting the intermediate values of the scale.

The premise of an algorithmic model is to be able to consistently and uniformly perform product feature prioritization in a finite number of steps. The product feature prioritization algorithmic model is fundamentally built on qualifying a feature via four core parameters that can be supplemented with additional parameters and data if so desired.

The algorithmic model for product feature prioritization is comprised of the following four core parameters:

  • 1. Dependency—a measure of how dependent other features are on this feature
  • (High/Low).
  • 2. Fundamental—the product will not work properly without this feature, from a technical perspective (Yes/No).
  • 3. Differentiator—the feature is a key differentiator, relative to competing products (Yes/No).
  • 4. Importance—product management’s measure of importance of this feature, from a product marketing and/or product planning perspective (High/Low).

The product features matrix lists the product’s features, coupled with selected values for each of the four core parameters for each feature.

With this data it is now possible to calculate an interim support variable named Urgency, which is the estimated urgency of developing the feature. The urgency variable is calculated automatically as High for all features which are either Fundamental (Yes) or which have a High Dependency. The Differentiator parameter is held for reference purposes and possible future inclusion into the calculation formula.

The next step is to automatically calculate the Priority final variable for various Importance and Urgency combinations. The priority variable represents the prioritization level of the said feature and is calculated according to the following logic (variable combination and its meaning):

  • 1. P1—high importance and high urgency (this feature will be in product and as robust as possible).
  • 2. P2—high importance and low urgency (this feature will be in product, but implementation may be reduced or optimized).
  • 3. P3—low importance and high urgency (this feature will only be in product if time allows).
  • 4. P4—low importance and low urgency (this feature will wait until a future version).

After completing the attribution of values for the four core parameters for each feature, the product features matrix is ready and presents a list of prioritized product features. At this point, with the product’s features prioritized, it is also easier to group features to create distinct product versions and a roadmap.

Table 11.1 is an example of a Product Features Matrix with features prioritized via an algorithmic model:

Table 11.1 Example of a Product Features Matrix with Features Prioritized Via an Algorithmic Model

Feature

Dependency

Fundamental

Differentiator

Importance

Urgency

Priority

Feature A

High

Yes

Yes

High

High

P1

Feature B

Low

Yes

No

High

High

P1

Feature C

Low

No

Yes

High

Low

P2

Feature D

Low

Yes

Yes

Low

High

P3

Feature E

Low

No

Yes

Low

Low

P4

Summary

Product feature prioritization can be a daunting and exhausting task, disliked by many because of the perceived ambiguity and the undue negotiations that may accompany it. Yet with the right tool, the algorithmic model for product feature prioritization that is incorporated in the Blackblot PMTK Methodology , this extremely critical activity can be accomplished far more easily and faster than ever before.

 
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